The present invention relates generally to utility meters and more particularly to privacy protection for smart meters.
An inherent objective for the power industry is to match power consumption to power generation as closely as possible and to optimally use available power generation capacities. It is very difficult for power companies to reach an efficient utilization rate because often peak demand cannot be matched to peak generation capacity.
In many markets, a supply-and-demand framework is used to achieve desired utilization patterns. For example, a restaurant may put certain items on sale early in the evening (e.g., Happy Hour) to incentivize restaurant patrons to arrive early, or an airline may lower prices for flights with undesirable departure and arrival times so as to encourage travelers to travel at those times instead of during peak hours, etc.
The power industry is particularly vulnerable to demand and supply fluctuations. For example, during hot afternoons, most consumers would run their air conditioners simultaneously. Similarly, many households have similar patterns for when major appliances such as ovens, dishwashers, and washing machines are used. On the flip-side, power generation, in particular power generation from solar and wind power, can be very dynamic and is therefore particularly difficult to tailor to demand fluctuations. Even if traditional power generation plants can be operated to follow demand, a retail electricity provider (REP) must be able to have enough power available to meet its highest demand periods as well. Thus, the power generation available to it will almost always be under-utilized as the peak demand is only likely to occur during a small portion of a few days per year.
Traditional electricity meters only measure the total power consumption of an account within a given time period and are unable to convey any information about consumption patterns at a particular metered location. Because total consumption is the only available information provided by these meters, it is very difficult for an REP to accurately measure or anticipate demand patterns by particular households or businesses. Furthermore, if an REP wanted to encourage consumption during certain times, e.g., to urge consumers to run appliances at night or to levy heavier charges for excessively cooling their homes during peak hours, a traditional meter that only measures total consumption would not support that type of charging mechanism.
One variety of traditional meters, so-called dual-rate meters, provide a limited amount of tariff flexibility. A rate-changer time switch keeps track of the time and switches between two registers depending on the time of day or night. While this makes it possible for an electricity provider to set different rates for different times, it does not provide enough granularity to effectively influence electricity demand. Furthermore, such meters do not provide a mechanism by which electricity rates may be changed as demand varies.
Though a retail electricity provider may have to pay increased rates to its suppliers during peak loads, it has no mechanism to transfer that increased cost to particular users who contributed to that peak load without penalizing those users who did not. The REP can only average out the increased cost of the peak load over its entire customer base. To ameliorate that unfairness, it is therefore also desirable to have a mechanism that provides a way to link levels of power usage to particularly costly periods and on to particular customers.
Smart meter and smart grid are complementary technologies that attempt to address the aforementioned desire to match consumption and generation. A smart meter collects energy usage information in small increments and transmits these measurements to the retail electricity provider. These increments may be every fifteen minutes, every 15 seconds, or even as often as every second. Retail energy providers, power generation companies, data warehouses, third-party service providers, and other entities desire access to this usage information, either in real-time or as a next-day report, for a variety of purposes. For example, utility companies trade and hedge energy contracts based on the real-time energy consumption and forecasts for next-day and near-future consumption. Fine-grained consumption patterns may be very useful in pricing such contracts. The retail energy provider may use the fine-grained usage information to bill the consumer at different rates based on time-of-day and quantities of consumption. Furthermore, the electricity market players may use the fine-grained information in determining future market prices for electricity.
A smart meter contains, at a minimum, the following connected components:                a communications unit, e.g., an RF transceiver or network card, for communicating with other nodes located on a smart grid, such as a utility service provider and for communications to the utility consumers private network        a mechanism for measuring utility consumption at the metered node        a processor for executing program instructions controlling the operations of the smart meter        a memory for storing meter readings and program instructions        a remote switch for remotely cutting off the utility service        access to in-house switches to turn off particular appliances during peak load (high price) periods        
A smart grid is a utility grid that makes use of available information, for example, as provided by smart meters including bidirectional communication with the smart meter, to more efficiently balance the load on a utility grid by providing consumers with incentives to change consumption patterns to take advantage of a varying rate schedule that reflects generation costs as well as demand. As an example, electricity is traditionally sold on the retail level at either one rate schedule or using two rates—a day rate and a night rate. However, wholesale electricity is priced in much smaller increments, e.g., they may vary by the hour or less. Thus, there is a disconnect between the cost of the electricity to the retail electricity provider and the retail prices that the same retail electricity provider may charge. In a smart grid, pricing structures may vary by demand and allow the retail utility service provider to incentivize consumers to practice certain utility usage behaviors and to have customer billing reflect the actual cost to the utility of the consumed commodity.
To even further aid the consumer in taking advantage of a smart rate structure, a smart meter may be provisioned with mechanisms for running certain appliances only during inexpensive periods. This may be particularly important as more and more consumers switch to electric cars who would then have the option to charge their cars when electricity is cheaper.
While smart meters and smart grids may provide some tangible benefits to electricity providers and consumers alike, there are several important negative considerations speaking against smart meter deployment, including risk of loss of privacy to the consumer, risk of electronic vandalism, and fraud.
It has been demonstrated that with fine-grained utility metering, particularly electricity, it is possible to analyze the consumption in a way that would infringe on the consumer's privacy. An example of the privacy concerns was illustrated by researchers Dario Carluccio and Stephan Brinkhaus who demonstrated that it is possible to analyze a consumer's usage pattern, using two-second relay patterns, to determine which movie a consumer has watched. Carluccio, Dario, and Stephan Brinkhaus, Smart Hacking for Privacy, talk presented at 28th Chaos Communication Congress (28C3), www*youtube*com/watch?v=YYe4SwQn2 GE, visited on Dec. 17, 2012.
Most smart meters contain mechanisms to switch off utility service to a metered site. This remote access presents a hacking opportunity in which an attacker could maliciously turn off power (or other utility service) at select sites or entire sub-grids. Naturally, such attacks could have dire security implications.
Furthermore, because smart meters may be network nodes on a relatively open network, there is a risk that the smart meters may be hacked to manipulate consumption data or rate schedules. For example, the researchers Carluccio and Brinkhaus also demonstrated this point by manipulating the data and returning a reading to the utility company showing a negative quantity of power consumption.
Microsoft research has proposed methods for privacy-friendly smart metering (Microsoft, “Privacy-Friendly Smart Metering,” http://research*microsoft*com/en-us/projects/privacy_in_metering/1, accessed on Dec. 3, 2012). The smart meter certifies the energy readings by digitally signing the data, which results in “certified readings.” The meter can also encrypt the readings. To protect consumer privacy, the certified readings never leave the home boundary, which consists of the meter, user's computer, display, smart phone, and/or other user devices. The smart meter or the consumer devices perform the computations on the certified readings for various purposes, such as billing, and send the results to the energy provider or other third parties. In order to see the energy consumption, the consumers obtain the encryption keys from their readers and, hence, enable their devices to decrypt the data. The advantage of the Microsoft approach is protection of consumer privacy. However, in practice managing diverse client software for a potentially increasing number of applications on myriad client devices has been proven difficult. 1To avoid having impermissible functioning hyperlinks in this document, periods (“.”) in urls are replaced with asterisks (“*”). Thus, each asterisk should be replaced with a period when accessing the referenced site.
From the foregoing it will be apparent that while smart meters provide many desirable benefits there is still a need for an improved method to provide increased security to smart meters to enhance consumer privacy and to minimize the risk of fraud and malicious attacks that may impact security and welfare.